
import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowercase    :   List[str]		       =				re.compile(R"""\b(a|an|the)\b""", re.UNICODE)

lowercase    :   Optional[int]		       =				None







def 		_snake_case(    )					->       Optional[Any]:
       lowercase				:	List[Any]			      = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""							)
       parser.add_argument("""data_file"""   ,      metavar="""data.json"""   ,      help="""Input data JSON file."""							)
       parser.add_argument("""pred_file"""   ,      metavar="""pred.json"""   ,      help="""Model predictions."""							)
       parser.add_argument(
           """--out-file"""   ,      """-o"""   ,      metavar="""eval.json"""   ,      help="""Write accuracy metrics to file (default is stdout)."""							)
       parser.add_argument(
           """--na-prob-file"""   ,      """-n"""   ,      metavar="""na_prob.json"""   ,      help="""Model estimates of probability of no answer."""							)
       parser.add_argument(
           """--na-prob-thresh"""   ,      """-t"""   ,      type=SCREAMING_SNAKE_CASE__   ,      default=1.0   ,      help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""   ,      )
       parser.add_argument(
           """--out-image-dir"""   ,      """-p"""   ,      metavar="""out_images"""   ,      default=SCREAMING_SNAKE_CASE__   ,      help="""Save precision-recall curves to directory."""							)
       parser.add_argument("""--verbose"""   ,      """-v"""   ,      action="""store_true"""							)
       if len(sys.argv							) == 1:
              parser.print_help()
              sys.exit(1							)
       return parser.parse_args()







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       Optional[Any]:
       lowercase				:	Optional[Any]			      = {}
       for article in dataset:
              for p in article["paragraphs"]:
                     for qa in p["qas"]:
                            lowercase				:	Union[str, Any]			      = bool(qa["""answers"""]["""text"""]							)
       return qid_to_has_ans







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       Dict:

       def remove_articles(SCREAMING_SNAKE_CASE__							):
              return ARTICLES_REGEX.sub(""" """   ,      SCREAMING_SNAKE_CASE__							)

       def white_space_fix(SCREAMING_SNAKE_CASE__							):
              return " ".join(text.split()							)

       def remove_punc(SCREAMING_SNAKE_CASE__							):
              lowercase				:	Dict			      = set(string.punctuation							)
              return "".join(ch for ch in text if ch not in exclude							)

       def lower(SCREAMING_SNAKE_CASE__							):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__							)							)							)							)







def 		_snake_case(    SCREAMING_SNAKE_CASE__							)					->       Tuple:
       if not s:
              return []
       return normalize_answer(SCREAMING_SNAKE_CASE__							).split()







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       List[str]:
       return int(normalize_answer(SCREAMING_SNAKE_CASE__							) == normalize_answer(SCREAMING_SNAKE_CASE__							)							)







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       str:
       lowercase				:	Tuple			      = get_tokens(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Union[str, Any]			      = get_tokens(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Dict			      = collections.Counter(SCREAMING_SNAKE_CASE__							) & collections.Counter(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Optional[int]			      = sum(common.values()							)
       if len(SCREAMING_SNAKE_CASE__							) == 0 or len(SCREAMING_SNAKE_CASE__							) == 0:
              # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
              return int(gold_toks == pred_toks							)
       if num_same == 0:
              return 0
       lowercase				:	int			      = 1.0 * num_same / len(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Any			      = 1.0 * num_same / len(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Any			      = (2 * precision * recall) / (precision + recall)
       return fa







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Union[str, Any]:
       lowercase				:	List[Any]			      = {}
       lowercase				:	str			      = {}
       for article in dataset:
              for p in article["paragraphs"]:
                     for qa in p["qas"]:
                            lowercase				:	Optional[Any]			      = qa['''id''']
                            lowercase				:	List[str]			      = [t for t in qa['''answers''']['''text'''] if normalize_answer(SCREAMING_SNAKE_CASE__							)]
                            if not gold_answers:
                                   # For unanswerable questions, only correct answer is empty string
                                   lowercase				:	Tuple			      = ['''''']
                            if qid not in preds:
                                   print(f"Missing prediction for {qid}"							)
                                   continue
                            lowercase				:	Tuple			      = preds[qid]
                            # Take max over all gold answers
                            lowercase				:	List[Any]			      = max(compute_exact(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							) for a in gold_answers							)
                            lowercase				:	int			      = max(compute_fa(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							) for a in gold_answers							)
       return exact_scores, fa_scores







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Optional[int]:
       lowercase				:	Tuple			      = {}
       for qid, s in scores.items():
              lowercase				:	Union[str, Any]			      = na_probs[qid] > na_prob_thresh
              if pred_na:
                     lowercase				:	List[str]			      = float(not qid_to_has_ans[qid]							)
              else:
                     lowercase				:	Any			      = s
       return new_scores







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__=None							)					->       int:
       if not qid_list:
              lowercase				:	str			      = len(SCREAMING_SNAKE_CASE__							)
              return collections.OrderedDict(
                  [
                      ("""exact""", 100.0 * sum(exact_scores.values()							) / total),
                      ("""f1""", 100.0 * sum(fa_scores.values()							) / total),
                      ("""total""", total),
                  ]							)
       else:
              lowercase				:	Dict			      = len(SCREAMING_SNAKE_CASE__							)
              return collections.OrderedDict(
                  [
                      ("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list							) / total),
                      ("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list							) / total),
                      ("""total""", total),
                  ]							)







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Dict:
       for k in new_eval:
              lowercase				:	str			      = new_eval[k]







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Optional[int]:
       plt.step(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      color="""b"""   ,      alpha=0.2   ,      where="""post"""							)
       plt.fill_between(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      step="""post"""   ,      alpha=0.2   ,      color="""b"""							)
       plt.xlabel("""Recall"""							)
       plt.ylabel("""Precision"""							)
       plt.xlim([0.0, 1.05]							)
       plt.ylim([0.0, 1.05]							)
       plt.title(SCREAMING_SNAKE_CASE__							)
       plt.savefig(SCREAMING_SNAKE_CASE__							)
       plt.clf()







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__=None   ,      SCREAMING_SNAKE_CASE__=None							)					->       Any:
       lowercase				:	Optional[Any]			      = sorted(SCREAMING_SNAKE_CASE__   ,      key=lambda SCREAMING_SNAKE_CASE__							: na_probs[k]							)
       lowercase				:	int			      = 0.0
       lowercase				:	List[Any]			      = 1.0
       lowercase				:	Optional[int]			      = 0.0
       lowercase				:	Optional[Any]			      = [1.0]
       lowercase				:	List[Any]			      = [0.0]
       lowercase				:	Dict			      = 0.0
       for i, qid in enumerate(SCREAMING_SNAKE_CASE__							):
              if qid_to_has_ans[qid]:
                     true_pos += scores[qid]
              lowercase				:	List[Any]			      = true_pos / float(i + 1							)
              lowercase				:	Union[str, Any]			      = true_pos / float(SCREAMING_SNAKE_CASE__							)
              if i == len(SCREAMING_SNAKE_CASE__							) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
                     # i.e., if we can put a threshold after this point
                     avg_prec += cur_p * (cur_r - recalls[-1])
                     precisions.append(SCREAMING_SNAKE_CASE__							)
                     recalls.append(SCREAMING_SNAKE_CASE__							)
       if out_image:
              plot_pr_curve(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       return {"ap": 100.0 * avg_prec}







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       List[Any]:
       if out_image_dir and not os.path.exists(SCREAMING_SNAKE_CASE__							):
              os.makedirs(SCREAMING_SNAKE_CASE__							)
       lowercase				:	Any			      = sum(1 for v in qid_to_has_ans.values() if v							)
       if num_true_pos == 0:
              return
       lowercase				:	Union[str, Any]			      = make_precision_recall_eval(
           SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      out_image=os.path.join(SCREAMING_SNAKE_CASE__   ,      """pr_exact.png"""							)   ,      title="""Precision-Recall curve for Exact Match score"""   ,      )
       lowercase				:	List[str]			      = make_precision_recall_eval(
           SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      out_image=os.path.join(SCREAMING_SNAKE_CASE__   ,      """pr_f1.png"""							)   ,      title="""Precision-Recall curve for F1 score"""   ,      )
       lowercase				:	Dict			      = {k: float(SCREAMING_SNAKE_CASE__							) for k, v in qid_to_has_ans.items()}
       lowercase				:	Optional[int]			      = make_precision_recall_eval(
           SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      out_image=os.path.join(SCREAMING_SNAKE_CASE__   ,      """pr_oracle.png"""							)   ,      title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""   ,      )
       merge_eval(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      """pr_exact"""							)
       merge_eval(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      """pr_f1"""							)
       merge_eval(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      """pr_oracle"""							)







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Dict:
       if not qid_list:
              return
       lowercase				:	List[Any]			      = [na_probs[k] for k in qid_list]
       lowercase				:	Union[str, Any]			      = np.ones_like(SCREAMING_SNAKE_CASE__							) / float(len(SCREAMING_SNAKE_CASE__							)							)
       plt.hist(SCREAMING_SNAKE_CASE__   ,      weights=SCREAMING_SNAKE_CASE__   ,      bins=20   ,      range=(0.0, 1.0)							)
       plt.xlabel("""Model probability of no-answer"""							)
       plt.ylabel("""Proportion of dataset"""							)
       plt.title(f"Histogram of no-answer probability: {name}"							)
       plt.savefig(os.path.join(SCREAMING_SNAKE_CASE__   ,      f"na_prob_hist_{name}.png"							)							)
       plt.clf()







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Optional[Any]:
       lowercase				:	Tuple			      = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]							)
       lowercase				:	Optional[Any]			      = num_no_ans
       lowercase				:	int			      = cur_score
       lowercase				:	List[Any]			      = 0.0
       lowercase				:	Dict			      = sorted(SCREAMING_SNAKE_CASE__   ,      key=lambda SCREAMING_SNAKE_CASE__							: na_probs[k]							)
       for i, qid in enumerate(SCREAMING_SNAKE_CASE__							):
              if qid not in scores:
                     continue
              if qid_to_has_ans[qid]:
                     lowercase				:	Tuple			      = scores[qid]
              else:
                     if preds[qid]:
                            lowercase				:	Optional[int]			      = -1
                     else:
                            lowercase				:	int			      = 0
              cur_score += diff
              if cur_score > best_score:
                     lowercase				:	Dict			      = cur_score
                     lowercase				:	Dict			      = na_probs[qid]
       return 100.0 * best_score / len(SCREAMING_SNAKE_CASE__							), best_thresh







def 		_snake_case(    SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)					->       Tuple:
       lowercase				:	List[Any]			      = find_best_thresh(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       lowercase				:	Optional[int]			      = find_best_thresh(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       lowercase				:	List[str]			      = best_exact
       lowercase				:	List[Any]			      = exact_thresh
       lowercase				:	Any			      = best_fa
       lowercase				:	str			      = fa_thresh







def 		_snake_case(    )					->       Tuple:
       with open(OPTS.data_file							) as f:
              lowercase				:	Optional[int]			      = json.load(SCREAMING_SNAKE_CASE__							)
              lowercase				:	str			      = dataset_json['''data''']
       with open(OPTS.pred_file							) as f:
              lowercase				:	List[Any]			      = json.load(SCREAMING_SNAKE_CASE__							)
       if OPTS.na_prob_file:
              with open(OPTS.na_prob_file							) as f:
                     lowercase				:	List[str]			      = json.load(SCREAMING_SNAKE_CASE__							)
       else:
              lowercase				:	List[str]			      = {k: 0.0 for k in preds}
       lowercase				:	Optional[Any]			      = make_qid_to_has_ans(SCREAMING_SNAKE_CASE__							)  # maps qid to True/False
       lowercase				:	Optional[int]			      = [k for k, v in qid_to_has_ans.items() if v]
       lowercase				:	Dict			      = [k for k, v in qid_to_has_ans.items() if not v]
       lowercase				:	List[Any]			      = get_raw_scores(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       lowercase				:	Optional[int]			      = apply_no_ans_threshold(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      OPTS.na_prob_thresh							)
       lowercase				:	Tuple			      = apply_no_ans_threshold(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      OPTS.na_prob_thresh							)
       lowercase				:	int			      = make_eval_dict(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       if has_ans_qids:
              lowercase				:	Optional[int]			      = make_eval_dict(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      qid_list=SCREAMING_SNAKE_CASE__							)
              merge_eval(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      """HasAns"""							)
       if no_ans_qids:
              lowercase				:	Dict			      = make_eval_dict(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      qid_list=SCREAMING_SNAKE_CASE__							)
              merge_eval(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      """NoAns"""							)
       if OPTS.na_prob_file:
              find_all_best_thresh(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       if OPTS.na_prob_file and OPTS.out_image_dir:
              run_precision_recall_analysis(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      OPTS.out_image_dir							)
              histogram_na_prob(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      OPTS.out_image_dir   ,      """hasAns"""							)
              histogram_na_prob(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      OPTS.out_image_dir   ,      """noAns"""							)
       if OPTS.out_file:
              with open(OPTS.out_file   ,      """w"""							) as f:
                     json.dump(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__							)
       else:
              print(json.dumps(SCREAMING_SNAKE_CASE__   ,      indent=2							)							)


if __name__ == "__main__":
  lowercase    :   Union[str, Any]		       =				parse_args()
  if OPTS.out_image_dir:
    import matplotlib

    matplotlib.use("""Agg""")
    import matplotlib.pyplot as plt
  main()








'''simple docstring'''





import collections
import os
import re
from pathlib import Path


_UpperCamelCase						:						Optional[int]  =					'src/transformers'


# Matches is_xxx_available()
_UpperCamelCase						:						int  =					re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_UpperCamelCase						:						Dict  =					re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_UpperCamelCase						:						List[str]  =					re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
_UpperCamelCase						:						Any  =					re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
_UpperCamelCase						:						Dict  =					re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_UpperCamelCase						:						Tuple  =					re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
_UpperCamelCase						:						Union[str, Any]  =					re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_UpperCamelCase						:						str  =					re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
_UpperCamelCase						:						Union[str, Any]  =					re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
_UpperCamelCase						:						Optional[int]  =					re.compile(R'^\s*try:')
# Catches a line with else:
_UpperCamelCase						:						List[Any]  =					re.compile(R'^\s*else:')
def __UpperCAmelCase   (					A					:			Any		)    ->   Tuple:
			if _re_test_backend.search(A		) is None:
						return None
			UpperCAmelCase_      :       str          =       [b[0] for b in _re_backend.findall(A		)]
			backends.sort()
			return "_and_".join(A		)
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[int]:
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       str          =       f.readlines()

			UpperCAmelCase_      :       List[str]          =       0
			while line_index < len(A		) and not lines[line_index].startswith('''_import_structure = {'''		):
						line_index += 1

			# If this is a traditional init, just return.
			if line_index >= len(A		):
						return None

			# First grab the objects without a specific backend in _import_structure
			UpperCAmelCase_      :       Tuple          =       []
			while not lines[line_index].startswith('''if TYPE_CHECKING'''		) and find_backend(lines[line_index]		) is None:
						UpperCAmelCase_      :       List[Any]          =       lines[line_index]
						# If we have everything on a single line, let's deal with it.
						if _re_one_line_import_struct.search(A		):
									UpperCAmelCase_      :       str          =       _re_one_line_import_struct.search(A		).groups()[0]
									UpperCAmelCase_      :       Any          =       re.findall(r'''\[([^\]]+)\]'''			,       A		)
									for imp in imports:
												objects.extend([obj[1:-1] for obj in imp.split(''', '''		)]		)
									line_index += 1
									continue
						UpperCAmelCase_      :       Dict          =       _re_import_struct_key_value.search(A		)
						if single_line_import_search is not None:
									UpperCAmelCase_      :       List[str]          =       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', '''		) if len(A		) > 0]
									objects.extend(A		)
						elif line.startswith(''' ''' * 8 + '''"'''		):
									objects.append(line[9:-3]		)
						line_index += 1

			UpperCAmelCase_      :       int          =       {'''none''': objects}
			# Let's continue with backend-specific objects in _import_structure
			while not lines[line_index].startswith('''if TYPE_CHECKING'''		):
						# If the line is an if not is_backend_available, we grab all objects associated.
						UpperCAmelCase_      :       Any          =       find_backend(lines[line_index]		)
						# Check if the backend declaration is inside a try block:
						if _re_try.search(lines[line_index - 1]		) is None:
									UpperCAmelCase_      :       int          =       None

						if backend is not None:
									line_index += 1

									# Scroll until we hit the else block of try-except-else
									while _re_else.search(lines[line_index]		) is None:
												line_index += 1

									line_index += 1

									UpperCAmelCase_      :       str          =       []
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 4		):
												UpperCAmelCase_      :       Optional[Any]          =       lines[line_index]
												if _re_import_struct_add_one.search(A		) is not None:
															objects.append(_re_import_struct_add_one.search(A		).groups()[0]		)
												elif _re_import_struct_add_many.search(A		) is not None:
															UpperCAmelCase_      :       Dict          =       _re_import_struct_add_many.search(A		).groups()[0].split(''', '''		)
															UpperCAmelCase_      :       Dict          =       [obj[1:-1] for obj in imports if len(A		) > 0]
															objects.extend(A		)
												elif _re_between_brackets.search(A		) is not None:
															UpperCAmelCase_      :       int          =       _re_between_brackets.search(A		).groups()[0].split(''', '''		)
															UpperCAmelCase_      :       List[Any]          =       [obj[1:-1] for obj in imports if len(A		) > 0]
															objects.extend(A		)
												elif _re_quote_object.search(A		) is not None:
															objects.append(_re_quote_object.search(A		).groups()[0]		)
												elif line.startswith(''' ''' * 8 + '''"'''		):
															objects.append(line[9:-3]		)
												elif line.startswith(''' ''' * 1_2 + '''"'''		):
															objects.append(line[1_3:-3]		)
												line_index += 1

									UpperCAmelCase_      :       int          =       objects
						else:
									line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
			UpperCAmelCase_      :       Union[str, Any]          =       []
			while (
			    line_index < len(A		)
			    and find_backend(lines[line_index]		) is None
			    and not lines[line_index].startswith('''else'''		)
			):
						UpperCAmelCase_      :       int          =       lines[line_index]
						UpperCAmelCase_      :       int          =       _re_import.search(A		)
						if single_line_import_search is not None:
									objects.extend(single_line_import_search.groups()[0].split(''', '''		)		)
						elif line.startswith(''' ''' * 8		):
									objects.append(line[8:-2]		)
						line_index += 1

			UpperCAmelCase_      :       int          =       {'''none''': objects}
			# Let's continue with backend-specific objects
			while line_index < len(A		):
						# If the line is an if is_backend_available, we grab all objects associated.
						UpperCAmelCase_      :       Optional[int]          =       find_backend(lines[line_index]		)
						# Check if the backend declaration is inside a try block:
						if _re_try.search(lines[line_index - 1]		) is None:
									UpperCAmelCase_      :       Optional[int]          =       None

						if backend is not None:
									line_index += 1

									# Scroll until we hit the else block of try-except-else
									while _re_else.search(lines[line_index]		) is None:
												line_index += 1

									line_index += 1

									UpperCAmelCase_      :       str          =       []
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 8		):
												UpperCAmelCase_      :       Tuple          =       lines[line_index]
												UpperCAmelCase_      :       Tuple          =       _re_import.search(A		)
												if single_line_import_search is not None:
															objects.extend(single_line_import_search.groups()[0].split(''', '''		)		)
												elif line.startswith(''' ''' * 1_2		):
															objects.append(line[1_2:-2]		)
												line_index += 1

									UpperCAmelCase_      :       Optional[Any]          =       objects
						else:
									line_index += 1

			return import_dict_objects, type_hint_objects
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Union[str, Any]		)    ->   Optional[Any]:

			def find_duplicates(A					:			Optional[int]		):
						return [k for k, v in collections.Counter(A		).items() if v > 1]

			if list(import_dict_objects.keys()		) != list(type_hint_objects.keys()		):
						return ["Both sides of the init do not have the same backends!"]

			UpperCAmelCase_      :       str          =       []
			for key in import_dict_objects.keys():
						UpperCAmelCase_      :       Union[str, Any]          =       find_duplicates(import_dict_objects[key]		)
						if duplicate_imports:
									errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"		)
						UpperCAmelCase_      :       Optional[int]          =       find_duplicates(type_hint_objects[key]		)
						if duplicate_type_hints:
									errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"		)

						if sorted(set(import_dict_objects[key]		)		) != sorted(set(type_hint_objects[key]		)		):
									UpperCAmelCase_      :       Optional[int]          =       '''base imports''' if key == '''none''' else F"{key} backend"
									errors.append(F"Differences for {name}:"		)
									for a in type_hint_objects[key]:
												if a not in import_dict_objects[key]:
															errors.append(F"  {a} in TYPE_HINT but not in _import_structure."		)
									for a in import_dict_objects[key]:
												if a not in type_hint_objects[key]:
															errors.append(F"  {a} in _import_structure but not in TYPE_HINT."		)
			return errors
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Tuple          =       []
			for root, _, files in os.walk(A		):
						if "__init__.py" in files:
									UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''__init__.py'''		)
									UpperCAmelCase_      :       str          =       parse_init(A		)
									if objects is not None:
												UpperCAmelCase_      :       List[str]          =       analyze_results(*A		)
												if len(A		) > 0:
															UpperCAmelCase_      :       Optional[Any]          =       F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
															failures.append('''\n'''.join(A		)		)
			if len(A		) > 0:
						raise ValueError('''\n\n'''.join(A		)		)
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       Any          =       []
			for path, directories, files in os.walk(A		):
						for folder in directories:
									# Ignore private modules
									if folder.startswith('''_'''		):
												directories.remove(A		)
												continue
									# Ignore leftovers from branches (empty folders apart from pycache)
									if len(list((Path(A		) / folder).glob('''*.py'''		)		)		) == 0:
												continue
									UpperCAmelCase_      :       Union[str, Any]          =       str((Path(A		) / folder).relative_to(A		)		)
									UpperCAmelCase_      :       List[Any]          =       short_path.replace(os.path.sep			,       '''.'''		)
									submodules.append(A		)
						for fname in files:
									if fname == "__init__.py":
												continue
									UpperCAmelCase_      :       Optional[Any]          =       str((Path(A		) / fname).relative_to(A		)		)
									UpperCAmelCase_      :       Union[str, Any]          =       short_path.replace('''.py'''			,       ''''''		).replace(os.path.sep			,       '''.'''		)
									if len(submodule.split('''.'''		)		) == 1:
												submodules.append(A		)
			return submodules


_UpperCamelCase						:						List[Any]  =					[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]
def __UpperCAmelCase   (					)    ->   int:
			# This is to make sure the transformers module imported is the one in the repo.
			from transformers.utils import direct_transformers_import

			UpperCAmelCase_      :       Dict          =       direct_transformers_import(A		)

			UpperCAmelCase_      :       List[str]          =       set(transformers._import_structure.keys()		)
			# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
			# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
			# (potentiall re-) add them.
			with open(os.path.join(A			,       '''__init__.py'''		)			,       '''r'''		) as f:
						UpperCAmelCase_      :       Dict          =       f.read()
			import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]'''			,       A		)		)		)

			UpperCAmelCase_      :       Union[str, Any]          =       [
			    module
			    for module in get_transformers_submodules()
			    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
			]

			if len(A		) > 0:
						UpperCAmelCase_      :       Optional[Any]          =       '''\n'''.join(F"- {module}" for module in module_not_registered		)
						raise ValueError(
						    '''The following submodules are not properly registed in the main init of Transformers:\n'''
						    F"{list_of_modules}\n"
						    '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'''		)


if __name__ == "__main__":
						check_all_inits()
						check_submodules()


